Optimum management of multiple hospital operating rooms (OR) is a complex problem. For example, a large hospital such as the Houston Methodist Hospital has approximately seventy active ORs with a large number of procedures per day and per OR that need to be scheduled several weeks in advance. Each procedure requires gathering a team led by a surgeon for a specific block of time in the OR. But even the most standard procedure, such as a laparoscopic cholecystectomy (which account for approximately 600,000 cases per year in the United States), can exhibit a significant variation in time duration. It is often the case that multiple OR scheduling must be done under uncertainties on time duration. Some procedures may lead to fairly unpredictable events, such as unexpected bleeding or additional work that requires more time, and possibly more personnel and equipment. At the same time, physician and patient movement through the perioperative (pre, intra, and post-operative) space directly affects operating room efficiency and timeliness of scheduling.
While the OR is a complex, high technology setting, there is still not an automatic feedback loop between the surgical team and the OR system to allow real time projection, detection, and adjustment of previously made decisions in scheduling. It is believed that effective OR awareness could provide early signs of problems that can allow the OR management to reallocate resources in a more efficient way. Being able to project operating room statuses and patient locations automatically and in real time on a centralized display system will further allow for improved OR management efficiency and efficacy.
A system installed in the OR that has tracking capability of all key events in order to assess the working flow in multiple ORs and builds a statistical model with that data that can be used to rationalize decision making and resource allocation. While there have been numerous works investigating this issue, it seems that there has been no practical solution implemented yet to automatically collect the necessary data for this endeavor.
It has been recognized that OR time is one of the most significant budget expenses in a modern hospital. It is also recognized that delays in OR procedures due to lapses in scheduling and/or OR resources availability have been at least in part responsible for poor surgical outcomes.
Previous investigators (e.g. University of Iowa Prof. Franklin Dexter) have provided an extensive bibliography on OR management under various aspect such as rationale on economics, algorithmic methods to optimize the management, and necessary tools to predict surgery procedure duration. However, such disclosures do not provide systems and methods as disclosed herein utilizing appropriate sensors, modeling, and computer processing implementation. Current OR management software/hardware systems that offer patient and OR status displays rely on manual input of aforementioned statuses.
Previous investigations into OR management optimization typically reviewed OR allocation several days prior to surgery. The input flow of OR procedures to be achieved as well as the resources available (staff, OR, equipment, etc. . . . ) to do the work are assumed to be known. In these investigations, the problem is typically formalized mathematically and solved with some optimization algorithm. In addition, several assumptions are often made on the level of complexity of the problem, depending on the time scale, number of ORs and/or types of surgery. It is assumed that the data available—such as expected time for surgery, patient and staff availability—can be either deterministic or probabilistic with a certain level of uncertainties. In typical previous investigation, the panel of mathematical methods to solve the problem encompasses linear integer programming, petri nets, stochastic optimization, etc. Validation is often based either on simulation tools or true comparison between different methods of scheduling in clinical conditions. However, this work is often based on tedious data acquisition that is done manually, which can be an obstacle to going further and deeper in the OR management field. Exemplary embodiments disclosed herein provide systems and methods to address such issues.
Investigations into predicting OR task durations typically rely on extensive collection of data on OR activities. In such cases, one needs to decide about the level of details used in the description of the procedure, which can result in a statistical model that might be valid for the specific category of intervention only. The reliability of such a statistical model depends on the normalization of the procedure and the quality of service at the hospital. This in turn depends on the standard of the surgical team and might be available only to large volume procedure that offers enough reproducibility.
Prior techniques that have been used to record and annotate the OR activities include a video camera mounted in the light that is above the OR table. In addition, sometimes a fixed video camera may also be mounted on the wall of the OR. For minimally invasive surgery, the video output of the endoscope camera may also be projected and/or recorded. There have been numerous works in computer vision then that either concentrate on following the motion and movements of the surgical team in the OR, or the laparoscopic instrument in the abdominal cavity.
It is also possible to analyze the motion of the hand of the surgeon during the procedure. There is continuous progress made on pattern recognition. It is however, quite difficult to get such methods working with sufficient and consistent accuracy. A primary reason is that there is typically significant variability with in people motion. Tracking a specific event or individual may become unfeasible, due to obstruction of view, or with staff moving in and out of multiple ORs. Accordingly, a computer vision method for OR function tracking is presented with significant obstacles. Exemplary embodiments disclosed herein include systems and method based on distributed sensors to track specific events to address these and other issues.
Previous investigations have also addressed the tracking of OR functions at the surgical tool level. The field of laparoscopic surgery of large volume minimally invasive surgery is one example. In addition, extensive study based on pattern recognition of tools in that view has also been published. Furthermore, radio frequency identification (RFID) tracking of instruments has been a popular solution. However, the OR environment is not favorable to this technology for the tracking of instruments. Similarly, using a bar code on each laparoscopic instrument is also not considered a robust solution.
Therefore, a need in the art exists for a minimally intrusive, yet robust, systems and methods to track perioperative functions that define work flow from the physical as well as cognitive point of view and model perioperative and intraoperative flow to allow efficient multiple OR management scheduling and resource allocation.